Radiation treatment planning is the process creating a radiotherapy treatment plan for treating a tumor(s) via ionizing radiation. For treatment planning, the subject is scanned and the resulting volumetric image data is used as initial reference or planning image data to create a treatment plan for tumors or target volumes, segmented or contoured into regions of interest (ROI) in the image data. With radiotherapy, the prescribed radiation dose is delivered in a fractionated manner, with the radiation dose being divided and given over a number of treatments, which may span several weeks. Unfortunately, daily physiological changes (e.g., organ filling, weight loss, etc) can cause the tumor volume and surrounding anatomical structures to move and change in shape during the course of the therapy such that continuing to follow the initial plan may result in an actual received dose distribution the differs from the planned dose distribution. Similar modifications are needed in any plan for directing energy at particular tissue sites for which the map may change in the course of treatment, such as interferential therapy, transcutaneous electrical nerve stimulation, pulsed shortwave therapy, or laser therapy.
As a result, the initial plan should be adapted to match the new location and shape of the target volume and surrounding anatomical structures based on subsequently acquire image data such as image data acquired during a treatment session (in-treatment). Unfortunately, the workload involved in the such adaptive re-planning can be complex and time consuming as this involves delineation or segmentation of tissue of interest and anatomical structures from latest image data for the patient. In an alternative approach, a deformable registration is used to estimate a voxel-to-voxel mapping or transformation between initial image data and latest image data. Generally, such image registration is the process of aligning features in different images by applying a transformation to one of the images so that it matches the other. The transformation is used to propagate the contours in the initial image data to contours in the latest image data.
One such registration includes the Demons deformable image registration, which is described in Thirion, J. P., “Image matching as a diffusion process: an Analogy with Maxwell's demons,” Medical Image Analysis, 1988, volume 2, number 3, pp. 243-260. Generally, Demons deformable image registration takes two images as input and produces a displacement or deformation vector field (DVF) that indicates the transformation that should be applied to voxels of one of the images so that it can be aligned with the other image. The Demons deformable image registration is an intensity (i.e., gray level) based registration in which the DVF is calculated, based on optical flow and via an iterative process, from local image information only, using a matching process that climbs the gradient of image intensity either upward or downward, in the direction of maximum steepness, in a manner that matches the gray levels as quick as possible. Unfortunately, with only the image gradient to guide the deformation process, as in the Demons deformable image registration, sideways movement of voxels is uncontrolled. The direction of maximum steepness may not be the direction in which tissue actually moved, and (since it is computed by the noisy process of estimating a gradient, composed numerically approximated derivatives) may vary unsteadily. This can lead to lack of smoothness and hence to geometric discontinuity in the deformed image and jaggedness in the propagated contours.